A scalable low-cost internet of things-based electronic nose for identifying chemical ripening in fruits

Abstract

Indiscriminate use of chemical agents like calcium carbide and ethephon for the ripening of fruits poses grave health hazards, emitting carcinogenic and neurotoxic compounds. Here we present a new, scalable, inexpensive, internet of things (IoT)-enabled electronic nose (e-nose) AI-Bot system for the detection of chemically ripened fruits. This would involve the development of a system that uses an MQ-3 gas sensor to quantify the ethanol content, as well as an MQ-135 gas sensor with an ESP32 microcontroller to quantify even further the amount of volatile organic compounds (VOCs) suggestive of artificial ripening. Flutter-based mobile application allows real-time monitoring, ripening classification using machine learning (ML) algorithms, and logging the historical data. A small sample was taken for inter-document feature literature mining, modelling sensor behaviors according to voltage dividers and gas concentration resistance laws for robust calibration and classification performance. Validation studies were performed on mango, banana, and papaya fruit in the laboratory environment. Total 75 samples (25 each of banana, mango, papaya across 3 trials) of fruit were tested. The implemented system achieved 95% for banana, 92% for mango, and 90% classification accuracy for papaya when cross validated.